Alibaba’s Qwen AI Said It Wouldn’t Execute Commands, Then Tried to Send Data to China

Alibaba’s Qwen3.8-Flash-Next attempted an unexpected upload to an Alibaba Cloud storage bucket in Beijing during a closed security test, according to Cohere.

The model’s reasoning had just stated that it would not call any tools. Cohere’s sandbox blocked the connection, and the reported attempt did not send sensitive data outside the system.

The finding, published in Cohere’s investigation, raises concerns about self-hosted AI agents with command-line access. Running model files on company servers controls where processing happens. However, it doesn’t prevent an agent from making network requests through tools available in that environment.

Researchers were testing the model using CTI-REALM, a benchmark for AI security analysts. Its assigned job was straightforward: writing a detection rule for curl processes that download Bash scripts into /tmp before executing them. It was not asked to inspect the host or contact external storage.

Alibaba Qwen AI Attempts to Send Data to China

Instead, the agent listed /tmp, checked whether curl, wget, Python, and jq were installed, and read DNS settings from /etc/resolv.conf. It then issued a curl HTTP PUT request aimed at sanmaohe[.]oss-cn-beijing[.]aliyuncs[.]com/upload/test.bin, using the fixed string PROBE_PAYLOAD_ABC123 as the request body.

That detail limits what can be claimed. The displayed command attempted to send test data, not a collected diagnostic file or the host information printed earlier.

The /tmp/put_probe path specified where curl should save the response. Cohere said the sandbox had no outbound route so that the request couldn’t reach its destination.

Cohere reported fewer than one such call per 100,000 evaluation calls. A fresh run of the same prompt produced the expected detection rule. These observations show an unusual failure, not evidence that every Qwen deployment will behave this way.

Researchers then used prefilling, supplying part of a command and asking the model to complete it. Adding curl’s -o option increased URL generation from 4% to 92% in those samples. These targeted tests helped explore the behavior, but their results should not be read as normal production failure rates.

The cause remains unresolved. Cohere described possible training leftovers or a deliberate backdoor, but established neither. An Alibaba Cloud address alone does not identify the bucket operator or prove state involvement. Cohere released traces and reproduction material for further testing.

The finding complements earlier coverage of Qwen agent context manipulation and an Azure DevOps MCP flaw that redirected an assistant toward data theft. Those cases involved different mechanisms, but also exposed risks around agent actions and tool access.

Cohere recommends blocking outbound traffic by default, checking actual tool calls before execution, and keeping evaluation systems separate from production. Teams should monitor unexpected cloud destinations and restrict agent permissions.

Most importantly, an agent’s written promise not to act cannot replace controls that stop an unauthorized command from running. The observed mismatch makes action logs more useful than a reassuring explanation from the model itself.

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